14 citations · 21 across the 6 of their papers we have counts for
3 papers · 1 filter
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
Zhenwei Dai, Chen Dun, Yuxin Tang +2
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data. Currently, most of federated training schemes learns a global m…
ResIST: Layer-Wise Decomposition of ResNets for Distributed Training
Chen Dun, Cameron R. Wolfe, Christopher M. Jermaine +1
We propose ResIST, a novel distributed training protocol for Residual Networks (ResNets). ResIST randomly decomposes a global ResNet into several shallow sub-ResNets that are train…
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
Cameron R. Wolfe, Jingkang Yang, Arindam Chowdhury +4
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the num…